BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction
Abstract
:1. Introduction
2. Research Methodology
3. Bibliography Results
3.1. Annual Publication Trend of the FOCIT Literature
3.2. Contribution of Journals in FOCIT Literature
3.3. Research Instruments Utilized in the FOCIT Literature
3.4. Science Mapping of the Relevant Keywords from FOCIT Literature
4. Content Analysis and Critical Review
4.1. Strategic Horizons
4.2. Information Management Technologies
4.3. BIM and Its Integration with Information Analytic Technologies
4.4. Digital Twin (DT) and Cyber-Physical System (CPS)
4.5. Semantic and Ontology-Based Data Management
4.6. Life Cycle Information Management and Sustainability Concern
5. Visualization Techniques
5.1. Current State of the Art
5.2. The Role of Immersive Technologies
5.3. Visualization Techniques Prediction
5.4. Immersive Technologies in the Future
6. Discussion on the Roadmap for the Future
6.1. 4D Printing
6.2. Augmented, Virtual, and Mixed Reality
6.3. Cloud XR
6.4. Metaverse for AEC and VDC Professional (M-AEC)
6.5. AIoT (AI-Integrated IoT)
6.6. Autonomous Digital Twin
6.7. Automatic Guided Vehicles (AGV)
6.8. Exoskeletons
6.9. Construction Telematics and Neural Controlled Devices
7. Integrating Circular Economy (CE) with FOCIT
8. Mapping the Technologies
- Climate technology: refers to those technologies that were developed to reduce GHGs. DTs using AIoT, and machine learning can improve energy saving and monitor thermal energy consumption, waste heat, temperature, humidity, and light levels for maintaining occupant comfort and optimizing building operating costs.
- Resilient technology: refers to those technologies that are developed for the continuity of business during uncertain times or pandemics such as COVID-19. This can be a convergent technology that enables a business to have a minimum level of services and requirements during the unpredicted time. While the communication tools for office work and meetings were widely used during the pandemic, a high level of complexity was involved in continuing the construction operation and performance by using remote control technologies during the pandemic. This is an open question to the current demand of the construction business. However, answering this question needs further investigations examining various technical solutions, assessing the reliability and safety of these solutions, and offering many use cases to practitioners.
- Convergent technology: refers to a novel combination or integration of technologies that have the potential to enhance construction performance and operational tasks. The construction industry operates very similarly to how it did many years ago in most activities, and digital tools are used as single forms, mainly disconnected from other systems. The combination or integration provides an opportunity to develop innovative solutions to address various industry challenges. While previous literature was focused on single technology development, there is a long way to improve the practice of integrations and combinations. Shirowzhan et al. [149] discussed that compatibility and interoperability are key challenges that should be considered for convergent technology development.
- o Autonomous systems and machine-to-machine (M2M): integrated platforms with data exchangeability have not been fully extended and utilized. This helps construction laborers avoid dust and vibrations;
- o Skilled labor and human–machine interface (HMI): The industry needs skilled laborers to be familiar with machine languages with an understanding of data and robots. HMI will be a concern in terms of efficiency and safety;
- o Web of Things (WoT): while the Internet of Things (IoT) is accepted for use in construction projects, there are interoperability challenges among various IoT platforms and standards. This makes the data exchange challenging, and the project managers may need to use different platforms that decrease efficiency. The concept of WoT suggests connecting construction tools and equipment to the web, and the construction manager can detect efficiency and productivity under an online platform. This offers a high level of connectivity, real-time communication of objects, wireless asset trackers, smart health monitoring systems, and autonomous construction equipment where applicable.
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Reischauer, G. Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technol. Forecast. Soc. Change 2018, 132, 26–33. [Google Scholar] [CrossRef]
- Sepasgozar, S.M.E. Digital technology utilisation decisions for facilitating the implementation of Industry 4.0 technologies. Constr. Innov. 2020, 21, 476–489. [Google Scholar] [CrossRef]
- McKinsey & Company. How COVID 19 Has Pushed Companies over the Technology Tipping Point Final.pdf. 2020. Available online: https://www.mckinsey.com (accessed on 30 December 2022).
- McKinsey & Company. The COVID-19 Recovery Will Be Digital: A Plan for the First 90 Days. 2020. Available online: https://www.mckinsey.com (accessed on 30 December 2022).
- McKinsey & Company. How Disruption Is Reshaping the World’s Largest Ecosystem. 2020. Available online: https://www.mckinsey.com (accessed on 30 December 2022).
- Rani, H.A.; Farouk, A.M.; Anandh, K.S.; Almutairi, S.; Rahman, R.A. Impact of COVID-19 on Construction Projects: The Case of India. Buildings 2022, 12, 762. [Google Scholar] [CrossRef]
- Goldberg, B. Katerra Is Shutting Down 2021-06-02 Architectural Record.pdf. 2021. Available online: https://www.architecturalrecord.com (accessed on 30 December 2022).
- McKinsey & Company. The Next Normal in Construction Material Distribution. 2020. Available online: https://www.mckinsey.com (accessed on 30 December 2022).
- Allied Market Research. AEC Market Insights, Segment Analysis and Industry Forecast-2027. 2021. Available online: https://www.alliedmarketresearch.com (accessed on 30 December 2022).
- Sepasgozar, S.M.E. Digital twin and web-based virtual gaming technologies for online education: A case of construction management and engineering. Appl. Sci. 2020, 10, 4678. [Google Scholar] [CrossRef]
- Bello, S.A.; Oyedele, L.O.; Akinade, O.O.; Bilal, M.; Davila Delgado, J.M.; Akanbi, L.A.; Ajayi, A.O.; Owolabi, H.A. Cloud computing in construction industry: Use cases, benefits and challenges. Autom. Constr. 2021, 122, 103441. [Google Scholar] [CrossRef]
- Bosch-Sijtsema, P.; Claeson-Jonsson, C.; Johansson, M.; Roupe, M. The hype factor of digital technologies in AEC. Constr. Innov. 2021, 21, 1471–4175. [Google Scholar] [CrossRef]
- NATO. ST_Tech Trends Report 2020–2040.pdf. Available online: https://www.nato.int (accessed on 30 December 2022).
- Sepasgozar, S.M.E. Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment. Buildings 2021, 11, 151. [Google Scholar] [CrossRef]
- Teisserenc, B.; Sepasgozar, S.M. Software Architecture and Non-Fungible Tokens for Digital Twin Decentralized Applications in the Built Environment. Buildings 2022, 12, 1447. [Google Scholar] [CrossRef]
- Darko, A.; Chan, A.P.C.; Yang, Y.; Tetteh, M.O. Building information modeling (BIM)-based modular integrated construction risk management–Critical survey and future needs. Comput. Ind. 2020, 123, 103327. [Google Scholar] [CrossRef]
- Al-Mohammad, M.S.; Haron, A.T.; Esa, M.; Aloko, M.N.; Alhammadi, Y.; Anandh, K.S.; Rahman, R.A. Factors affecting BIM implementation: Evidence from countries with different income levels. Constr. Innov. 2022. Ahead of print. [Google Scholar] [CrossRef]
- Boje, C.; Guerriero, A.; Kubicki, S.; Rezgui, Y. Towards a semantic Construction Digital Twin: Directions for future research. Autom. Constr. 2020, 114, 103179. [Google Scholar] [CrossRef]
- IGI Global. What is Methodological Framework|IGI Global. Available online: https://www.igi-global.com (accessed on 30 December 2022).
- McMeekin, N.; Wu, O.; Germeni, E.; Briggs, A. How methodological frameworks are being developed: Evidence from a scoping review. BMC Med. Res. Methodol. 2020, 20, 173. [Google Scholar] [CrossRef]
- Fellows, R.F.; Liu, A.M.M. Wiley Research Methods for Construction, 5th ed.; Wiley-Blackwell: Hoboken, NJ, USA, 2021; ISBN 978-1-119-81473-3. Available online: https://www.wiley.com (accessed on 30 December 2022).
- Khan, A.; Sepasgozar, S.; Liu, T.; Yu, R. Integration of BIM and immersive technologies for AEC: A scientometric-SWOT analysis and critical content review. Buildings 2021, 11, 126. [Google Scholar] [CrossRef]
- Debrah, C.; Chan, A.P.C.; Darko, A. Artificial intelligence in green building. Autom. Constr. 2022, 137, 104192. [Google Scholar] [CrossRef]
- Khan, A.; Yu, R.; Liu, T.; Guan, H.; Oh, E. Drivers towards Adopting Modular Integrated Construction for Affordable Sustainable Housing: A Total Interpretive Structural Modelling (TISM) Method. Buildings 2022, 12, 637. [Google Scholar] [CrossRef]
- Wang, M.; Wang, C.C.; Sepasgozar, S.; Zlatanova, S. A systematic review of digital technology adoption in off-site construction: Current status and future direction towards industry 4.0. Buildings 2020, 10, 204. [Google Scholar] [CrossRef]
- Çetin, S.; De Wolf, C.; Bocken, N. Circular digital built environment: An emerging framework. Sustainability 2021, 13, 6348. [Google Scholar] [CrossRef]
- Yu, R.; Gu, N.; Ostwald, M.J. Technology, Cognition and Environments. In Computational Design; CRC Press: Boca Raton, FL, USA. [CrossRef]
- Sun, C. For architects, the Metaverse Is a Virgin Territory Full of Possibilities, and a Utopia without the Constraints of the Physical World.|Medium. Available online: https://chloesun.medium.com (accessed on 30 December 2022).
- Carra, G.; Magdani, N. Circular Business Models for the Built Environment. Available online: https://www.arup.com (accessed on 30 December 2022).
- Teisserenc, B.; Sepasgozar, S. Adoption of Blockchain Technology through Digital Twins in the Construction Industry 4.0: A PESTELS Approach. Buildings 2021, 11, 670. [Google Scholar] [CrossRef]
- Teisserenc, B.; Sepasgozar, S. Project Data Categorization, Adoption Factors, and Non-Functional Requirements for Blockchain Based Digital Twins in the Construction Industry 4.0. Buildings 2021, 11, 626. [Google Scholar] [CrossRef]
- Tezel, A.; Febrero, P.; Papadonikolaki, E.; Yitmen, I. Insights into Blockchain Implementation in Construction: Models for Supply Chain Management. J. Manag. Eng. 2021, 37, 1–41. [Google Scholar] [CrossRef]
- Li, X.; Lu, W.; Xue, F.; Wu, L.; Zhao, R.; Lou, J.; Xu, J. Blockchain-Enabled IoT-BIM Platform for Supply Chain Management in Modular Construction. J. Constr. Eng. Manag. 2022, 148, 04021195. [Google Scholar] [CrossRef]
- Lee, D.; Lee, S.H.; Masoud, N.; Krishnan, M.S.; Li, V.C. Integrated digital twin and blockchain framework to support accountable information sharing in construction projects. Autom. Constr. 2021, 127, 103688. [Google Scholar] [CrossRef]
- Yu, R.; Gu, N.; Lee, G.; Khan, A. A Systematic Review of Architectural Design Collaboration in Immersive Virtual Environments. Designs 2022, 6, 93. [Google Scholar] [CrossRef]
- PMI. Value of Project Management.pdf. Available online: https://www.pmi.org (accessed on 30 December 2022).
- Murtagh, N.; Scott, L.; Fan, J. Sustainable and resilient construction: Current status and future challenges. J. Clean. Prod. 2020, 268, 122264. [Google Scholar] [CrossRef]
- Myers, D. Construction Economics; Routledge: New York, NY, USA, 2017. [Google Scholar]
- Darian-Smith, E. Dying for the Economy: Disposable People and Economies of Death in the Global North. State Crime J. 2021, 10, 61–79. [Google Scholar] [CrossRef]
- Hirsch, P.B. The Great Discontent. J. Bus. Strategy 2021. Ahead-of-print. [Google Scholar] [CrossRef]
- Oti-Sarpong, K.; Pärn, E.A.; Burgess, G.; Zaki, M. Transforming the construction sector: An institutional complexity perspective. Constr. Innov. 2021, 22, 361–387. [Google Scholar] [CrossRef]
- Shan, M.; Hwang, B.G.; Zhu, L. A global review of sustainable construction project financing: Policies, practices, and research efforts. Sustainability 2017, 9, 2347. [Google Scholar] [CrossRef]
- Zhou, M.; Su, X.; Chen, Y.; An, L. Rapid construction and advanced technology for a COVID-19 field hospital in Wuhan, China. Proc. Inst. Civ. Eng. Civ. Eng. 2020, 174, 29–34. [Google Scholar] [CrossRef]
- Wubbeke, J.; Meissner, M.; Zenglein, M.J.; Ives, J.; Conrad, B. Made in China 2025. Available online: https://merics.org (accessed on 30 December 2022).
- Senate, U.S. BudgetCommitteeHistory2.pdf. Available online: https://www.budget.senate.gov (accessed on 30 December 2022).
- Abdelmageed, S.; Zayed, T. A study of literature in modular integrated construction-Critical review and future directions. J. Clean. Prod. 2020, 277, 124044. [Google Scholar] [CrossRef]
- Lasi, H.; Fettke, P.; Kemper, H.-G.; Feld, T.; Hoffmann, M. Industry 4.0. Bus. Inf. Syst. Eng. 2014, 6, 239–242. [Google Scholar] [CrossRef]
- Osterrieder, P.; Budde, L.; Friedli, T. The smart factory as a key construct of industry 4.0: A systematic literature review. Int. J. Prod. Econ. 2020, 221, 107476. [Google Scholar] [CrossRef]
- Smith, K.; Sepasgozar, S. Governance, Standards and Regulation: What Construction and Mining Need to Commit to Industry 4.0. Buildings 2022, 12, 1064. [Google Scholar] [CrossRef]
- Xu, Z.; Zayed, T.; Niu, Y. Comparative analysis of modular construction practices in mainland China, Hong Kong and Singapore. J. Clean. Prod. 2020, 245, 118861. [Google Scholar] [CrossRef]
- Kovacs, O. The dark corners of industry 4.0–Grounding economic governance 2.0. Technol. Soc. 2018, 55, 140–145. [Google Scholar] [CrossRef]
- Deng, M.; Menassa, C.C.; Kamat, V. From BIM to digital twins: A systematic review of the evolution of intelligent building representations in the AEC-FM industry. J. Inf. Technol. Constr. 2021, 26, 58–83. [Google Scholar] [CrossRef]
- Wong, J.K.W.; Ge, J.; He, S.X. Digitisation in facilities management: A literature review and future research directions. Autom. Constr. 2018, 92, 312–326. [Google Scholar] [CrossRef]
- Rausch, C.; Lu, R.; Talebi, S.; Haas, C. Deploying 3D scanning based geometric digital twins during fabrication and assembly in offsite manufacturing. Int. J. Constr. Manag. 2021, 1–14. [Google Scholar] [CrossRef]
- Malagnino, A.; Montanaro, T.; Lazoi, M.; Sergi, I.; Corallo, A.; Patrono, L. Building Information Modeling and Internet of Things integration for smart and sustainable environments: A review. J. Clean. Prod. 2021, 312, 127716. [Google Scholar] [CrossRef]
- Gheisari, M.; Irizarry, J. Investigating human and technological requirements for successful implementation of a BIM-based mobile augmented reality environment in facility management practices. Facilities 2016, 34, 69–84. [Google Scholar] [CrossRef]
- Deng, Y.; Irizarry, J. Integrating 4D BIM and GIS for Construction Supply Chain Management. J. Constr. Eng. Manag. 2019, 145, 04019016. [Google Scholar] [CrossRef]
- Dave, B.; Buda, A.; Nurminen, A.; Främling, K. A framework for integrating BIM and IoT through open standards. Autom. Constr. 2018, 95, 35–45. [Google Scholar] [CrossRef]
- Das, M.; Tao, X.; Cheng, J.C.P. BIM security: A critical review and recommendations using encryption strategy and blockchain. Autom. Constr. 2021, 126, 103682. [Google Scholar] [CrossRef]
- Chen, Y.J.; Lai, Y.S.; Lin, Y.H. BIM-based augmented reality inspection and maintenance of fire safety equipment. Autom. Constr. 2020, 110, 103041. [Google Scholar] [CrossRef]
- Chen, H.; Hou, L.; Zhang, G.K.; Moon, S. Development of BIM, IoT and AR/VR technologies for fire safety and upskilling. Autom. Constr. 2021, 125, 103631. [Google Scholar] [CrossRef]
- Williams, G.; Gheisari, M.; Chen, P.J.; Irizarry, J. BIM2MAR: An efficient BIM translation to mobile augmented reality applications. J. Manag. Eng. 2014, 31, A4014009. [Google Scholar] [CrossRef]
- Wang, H.; Pan, Y.; Luo, X. Integration of BIM and GIS in sustainable built environment: A review and bibliometric analysis. Autom. Constr. 2019, 103, 41–52. [Google Scholar] [CrossRef]
- Nawari, N.O.; Ravindran, S. Blockchain and Building Information Modeling (BIM): Review and applications in post-disaster recovery. Buildings 2019, 9, 149. [Google Scholar] [CrossRef]
- He, R.; Li, M.; Gan, V.J.L.; Ma, J. BIM-enabled computerized design and digital fabrication of industrialized buildings: A case study. J. Clean. Prod. 2021, 278, 123505. [Google Scholar] [CrossRef]
- Alizadehsalehi, S.; Hadavi, A.; Huang, J.C. From BIM to extended reality in AEC industry. Autom. Constr. 2020, 116, 103254. [Google Scholar] [CrossRef]
- Niu, Y.; Lu, W.; Chen, K.; Huang, G.G.; Anumba, C. Smart construction objects. J. Comput. Civ. Eng. 2016, 30, 04015070. [Google Scholar] [CrossRef]
- Pauwels, P.; Zhang, S.; Lee, Y.C. Semantic web technologies in AEC industry: A literature overview. Autom. Constr. 2017, 73, 145–165. [Google Scholar] [CrossRef]
- Mannino, A.; Dejaco, M.C.; Re Cecconi, F. Building information modelling and internet of things integration for facility management-literature review and future needs. Appl. Sci. 2021, 11, 3062. [Google Scholar] [CrossRef]
- Tang, S.; Shelden, D.R.; Eastman, C.M.; Pishdad-Bozorgi, P.; Gao, X. A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Autom. Constr. 2019, 101, 127–139. [Google Scholar] [CrossRef]
- Shkundalov, D.; Vilutienė, T. Bibliometric analysis of building information modeling, geographic information systems and web environment integration. Autom. Constr. 2021, 128, 103757. [Google Scholar] [CrossRef]
- Machado, R.L.; Vilela, C. Conceptual framework for integrating bim and augmented reality in construction management. J. Civ. Eng. Manag. 2020, 26, 83–94. [Google Scholar] [CrossRef]
- Lu, Q.; Lee, S. Image-Based Technologies for Constructing As-Is Building Information Models for Existing Buildings. J. Comput. Civ. Eng. 2017, 31, 04017005. [Google Scholar] [CrossRef]
- Nawari, N.O.; Ravindran, S. Blockchain technology and BIM process: Review and potentialapplications. J. Inf. Technol. Constr. 2019, 24, 209–238. [Google Scholar]
- Zhu, J.; Wang, X.; Chen, M.; Wu, P.; Kim, M.J. Integration of BIM and GIS: IFC geometry transformation to shapefile using enhanced open-source approach. Autom. Constr. 2019, 106, 102859. [Google Scholar] [CrossRef]
- Su, T.; Li, H.; An, Y. A BIM and machine learning integration framework for automated property valuation. J. Build. Eng. 2021, 44, 102636. [Google Scholar] [CrossRef]
- Alizadehsalehi, S.; Yitmen, I. Digital twin-based progress monitoring management model through reality capture to extended reality technologies (DRX). Smart Sustain. Built Environ. 2021, 12, 200–236. [Google Scholar] [CrossRef]
- Ozturk, G.B. Digital Twin Research in the AECO-FM Industry. J. Build. Eng. 2021, 40, 102730. [Google Scholar] [CrossRef]
- Leitão, P.; Colombo, A.W.; Karnouskos, S. Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Comput. Ind. 2016, 81, 11–25. [Google Scholar] [CrossRef]
- Alshammari, K.; Beach, T.; Rezgui, Y. Cybersecurity for digital twins in the built environment: Current research and future directions. J. Inf. Technol. Constr. 2021, 26, 159–173. [Google Scholar] [CrossRef]
- Babiceanu, R.F.; Seker, R. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Comput. Ind. 2016, 81, 128–137. [Google Scholar] [CrossRef]
- Akanmu, A.A.; Anumba, C.J.; Ogunseiju, O.O. Towards next generation cyber-physical systems and digital twins for construction. J. Inf. Technol. Constr. 2021, 26, 505–525. [Google Scholar] [CrossRef]
- Jiang, W.; Ding, L.; Zhou, C. Cyber physical system for safety management in smart construction site. Eng. Constr. Archit. Manag. 2021, 28, 788–808. [Google Scholar] [CrossRef]
- Liu, G.; Yang, H.; Fu, Y.; Mao, C.; Xu, P.; Hong, J.; Li, R. Cyber-physical system-based real-time monitoring and visualization of greenhouse gas emissions of prefabricated construction. J. Clean. Prod. 2020, 246, 119059. [Google Scholar] [CrossRef]
- Patacas, J.; Dawood, N.; Kassem, M. BIM for facilities management: A framework and a common data environment using open standards. Autom. Constr. 2020, 120, 103366. [Google Scholar] [CrossRef]
- Tchouanguem Djuedja, J.F.; Abanda, F.H.; Kamsu-Foguem, B.; Pauwels, P.; Magniont, C.; Karray, M.H. An integrated Linked Building Data system: AEC industry case. Adv. Eng. Softw. 2021, 152, 102930. [Google Scholar] [CrossRef]
- Nazarenko, A.A.; Sarraipa, J.; Camarinha-Matos, L.M.; Garcia, O.; Jardim-Goncalves, R. Semantic data management for a virtual factory collaborative environment. Appl. Sci. 2019, 9, 4936. [Google Scholar] [CrossRef]
- Wong, K.D.; Fan, Q. Building information modelling (BIM) for sustainable building design. Facilities 2013, 31, 138–157. [Google Scholar] [CrossRef]
- Lim, Y.W.; Chong, H.Y.; Ling, P.C.H.; Tan, C.S. Greening existing buildings through Building Information Modelling: A review of the recent development. Build. Environ. 2021, 200, 107924. [Google Scholar] [CrossRef]
- Manzoor, B.; Othman, I.; Kang, J.M.; Geem, Z.W. Influence of building information modeling (BIM) implementation in high-rise buildings towards sustainability. Appl. Sci. 2021, 11, 7626. [Google Scholar] [CrossRef]
- Shi, Y.; Xu, J. BIM-based information system for econo-enviro-friendly end-of-life disposal of construction and demolition waste. Autom. Constr. 2021, 125, 103611. [Google Scholar] [CrossRef]
- Saieg, P.; Sotelino, E.D.; Nascimento, D.; Caiado, R.G.G. Interactions of Building Information Modeling, Lean and Sustainability on the Architectural, Engineering and Construction industry: A systematic review. J. Clean. Prod. 2018, 174, 788–806. [Google Scholar] [CrossRef]
- Meng, Q.; Zhang, Y.; Li, Z.; Shi, W.; Wang, J.; Sun, Y.; Xu, L.; Wang, X. A review of integrated applications of BIM and related technologies in whole building life cycle. Eng. Constr. Archit. Manag. 2020, 27, 1647–1677. [Google Scholar] [CrossRef]
- Olanrewaju, O.I.; Kineber, A.F.; Chileshe, N.; Edwards, D.J. Modelling the impact of building information modelling (BIM) implementation drivers and awareness on project lifecycle. Sustainability 2021, 13, 8887. [Google Scholar] [CrossRef]
- Abrishami, S.; Goulding, J.; Pour Rahimian, F.; Ganah, A. Virtual generative BIM workspace for maximising AEC conceptual design innovation: A paradigm of future opportunities. Constr. Innov. 2015, 15, 24–41. [Google Scholar] [CrossRef]
- Holzer, D. Design exploration supported by digital tool ecologies. Autom. Constr. 2016, 72, 3–8. [Google Scholar] [CrossRef]
- Loyola, M. Big data in building design: A review. J. Inf. Technol. Constr. 2018, 23, 259–284. [Google Scholar]
- Hussein, M.; Eltoukhy, A.E.E.; Karam, A.; Shaban, I.A.; Zayed, T. Modelling in off-site construction supply chain management: A review and future directions for sustainable modular integrated construction. J. Clean. Prod. 2021, 310, 127503. [Google Scholar] [CrossRef]
- Dzulkifli, N.; Sarbini, N.N.; Ibrahim, I.S.; Abidin, N.I.; Yahaya, F.M.; Nik Azizan, N.Z. Review on maintenance issues toward building maintenance management best practices. J. Build. Eng. 2021, 44, 102985. [Google Scholar] [CrossRef]
- Zhang, J.; Seet, B.C.; Lie, T.T. Building information modelling for smart built environments. Buildings 2015, 5, 100–115. [Google Scholar] [CrossRef]
- Li, C.Z.; Zhao, Y.; Xiao, B.; Yu, B.; Tam, V.W.Y.; Chen, Z.; Ya, Y. Research trend of the application of information technologies in construction and demolition waste management. J. Clean. Prod. 2020, 263, 121458. [Google Scholar] [CrossRef]
- Nikmehr, B.; Hosseini, M.R.; Wang, J.; Chileshe, N.; Rameezdeen, R. BIM-based tools for managing construction and demolition waste (CDW): A scoping review. Sustainability 2021, 13, 8427. [Google Scholar] [CrossRef]
- Alaloul, W.S.; Qureshi, A.H.; Musarat, M.A.; Saad, S. Evolution of close-range detection and data acquisition technologies towards automation in construction progress monitoring. J. Build. Eng. 2021, 43, 102877. [Google Scholar] [CrossRef]
- Afzal, M.; Shafiq, M.T.; Al Jassmi, H. Improving construction safety with virtual-design construction technologies-A review. J. Inf. Technol. Constr. 2021, 26, 319–340. [Google Scholar] [CrossRef]
- Guo, H.; Yu, Y.; Skitmore, M. Visualization technology-based construction safety management: A review. Autom. Constr. 2017, 73, 135–144. [Google Scholar] [CrossRef]
- Bademosi, F.; Blinn, N.; Issa, R.R.A. Use of augmented reality technology to enhance comprehension of construction assemblies. J. Inf. Technol. Constr. 2019, 24, 58–79. [Google Scholar]
- Álvares, J.S.; Costa, D.B.; Melo, R.R.S. Exploratory study of using unmanned aerial system imagery for construction site 3D mapping. Constr. Innov. 2018, 18, 301–320. [Google Scholar] [CrossRef]
- Hamzeh, F.; Abou-Ibrahim, H.; Daou, A.; Faloughi, M.; Kawwa, N. 3D visualization techniques in the AEC industry: The possible uses of holography. J. Inf. Technol. Constr. 2019, 24, 239–255. [Google Scholar]
- Pérez, G.; Escolà, A.; Rosell-Polo, J.R.; Coma, J.; Arasanz, R.; Marrero, B.; Cabeza, L.F.; Gregorio, E. 3D characterization of a Boston Ivy double-skin green building facade using a LiDAR system. Build. Environ. 2021, 206, 108320. [Google Scholar] [CrossRef]
- Niu, S.; Pan, W.; Zhao, Y. A virtual reality integrated design approach to improving occupancy information integrity for closing the building energy performance gap. Sustain. Cities Soc. 2016, 27, 275–286. [Google Scholar] [CrossRef]
- Marocco, M.; Garofolo, I. Integrating disruptive technologies with facilities management: A literature review and future research directions. Autom. Constr. 2021, 131, 103917. [Google Scholar] [CrossRef]
- Gontier, J.C.; Wong, P.S.P.; Teo, P. Towards the implementation of immersive technology in construction-A SWOT analysis. J. Inf. Technol. Constr. 2021, 26, 366–380. [Google Scholar]
- Zhang, Y.; Liu, H.; Kang, S.C.; Al-Hussein, M. Virtual reality applications for the built environment: Research trends and opportunities. Autom. Constr. 2020, 118, 103311. [Google Scholar] [CrossRef]
- Song, Y.; Koeck, R.; Luo, S. Review and analysis of augmented reality (AR) literature for digital fabrication in architecture. Autom. Constr. 2021, 128, 103762. [Google Scholar] [CrossRef]
- Ventura, S.M.; Castronovo, F.; Ciribini, A.L.C. A design review session protocol for the implementation of immersive virtual reality in usability-focused analysis. J. Inf. Technol. Constr. 2020, 25, 233–253. [Google Scholar]
- Sepasgozar, S.M.E.; Ghobadi, M.; Shirowzhan, S.; Edwards, D.J.; Delzendeh, E. Metrics development and modelling the mixed reality and digital twin adoption in the context of Industry 4.0. Eng. Constr. Archit. Manag. 2021, 28, 1355–1376. [Google Scholar] [CrossRef]
- Statista. Virtual Reality (VR)-Statistics & Facts|Statista. 2022. Available online: https://www.statista.com (accessed on 30 December 2022).
- Research, K. Augmented and Virtual Reality Market Size, Share, Trends, Reports & Global Forecast to 2033. 2022. Available online: https://www.marketsandmarkets.com (accessed on 30 December 2022).
- Mordor Intelligence. Augmented Reality & Mixed Reality Market Report|Size, Share, Growth & Trends (2023–2028). 2022. Available online: https://www.mordorintelligence.com (accessed on 30 December 2022).
- Facebook. Connect 2021: Our Vision for the Metaverse. 2021. Available online: https://tech.fb.com/connect-2021-our-vision-for-the-metaverse/ (accessed on 30 December 2022).
- Ogunnusi, M.; Hamma-Adama, M.; Salman, H.; Kouider, T. COVID-19 pandemic: The effects and prospects in the construction industry. Int. J. Real Estate Stud. 2020, 14, 120–128. Available online: https://www.utm.my/intrest/files/2020/11/2_Final_MS_CRES-Covid-025.pdf (accessed on 30 December 2022).
- Haleem, A.; Javaid, M.; Singh, R.P.; Suman, R. Significant roles of 4D printing using smart materials in the field of manufacturing. Adv. Ind. Eng. Polym. Res. 2021, 4, 301–311. [Google Scholar] [CrossRef]
- Besklubova, S.; Skibniewski, M.J.; Zhang, X. Factors Affecting 3D Printing Technology Adaptation in Construction. J. Constr. Eng. Manag. 2021, 147, 04021026. [Google Scholar] [CrossRef]
- Hossain, M.A.; Zhumabekova, A.; Paul, S.C.; Kim, J.R. A review of 3D printing in construction and its impact on the labor market. Sustainability 2020, 12, 8492. [Google Scholar] [CrossRef]
- Pan, Y.; Zhang, L. Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Autom. Constr. 2021, 122, 103517. [Google Scholar] [CrossRef]
- Yi, H. 4D-printed parametric façade in architecture: Prototyping a self-shaping skin using programmable two-way shape memory composite (TWSMC). Eng. Constr. Archit. Manag. 2021. Ahead-of-print. [Google Scholar] [CrossRef]
- Wen, J.; Gheisari, M. Using virtual reality to facilitate communication in the AEC domain: A systematic review. Constr. Innov. 2020, 20, 509–542. [Google Scholar] [CrossRef]
- Cheng, J.C.P.; Chen, K.; Chen, W. State-of-the-Art Review on Mixed Reality Applications in the AECO Industry. J. Constr. Eng. Manag. 2020, 146, 03119009. [Google Scholar] [CrossRef]
- Ghosh, A.; Edwards, D.J.; Hosseini, M.R. Patterns and trends in Internet of Things (IoT) research: Future applications in the construction industry. Eng. Constr. Archit. Manag. 2021, 28, 457–481. [Google Scholar] [CrossRef]
- Wang, X.; Wang, J.; Wu, C.; Xu, S.; Ma, W. Engineering Brain: Metaverse for future engineering. AI Civ. Eng. 2022, 1, 2. [Google Scholar] [CrossRef]
- Tsai, Y.-C. The Value Chain of Education Metaverse. arXiv 2022, arXiv:2211.05833. Available online: https://arxiv.org/abs/2211.05833 (accessed on 30 December 2022).
- Cho, Y.; Wang, C. Information Technology and the Built Environment. J. Constr. Eng. Manag. 2021, 147, 1–2. [Google Scholar] [CrossRef]
- Yang, Y.; Pan, W. Automated guided vehicles in modular integrated construction: Potentials and future directions. Constr. Innov. 2021, 21, 85–104. [Google Scholar] [CrossRef]
- Tan, Y.; Song, Y.; Zhu, J.; Long, Q.; Wang, X.; Cheng, J.C.P. Optimizing lift operations and vessel transport schedules for disassembly of multiple offshore platforms using BIM and GIS. Autom. Constr. 2018, 94, 328–339. [Google Scholar] [CrossRef]
- Pradhananga, P.; El Zomor, M.; Santi Kasabdji, G. Identifying the Challenges to Adopting Robotics in the US Construction Industry. J. Constr. Eng. Manag. 2021, 147, 05021003. [Google Scholar]
- Nnaji, C.; Karakhan, A.A. Technologies for safety and health management in construction: Current use, implementation benefits and limitations, and adoption barriers. J. Build. Eng. 2020, 29, 101212. [Google Scholar] [CrossRef]
- Trimble. Construction Asset Management Software-Trimble PULSE Telematics. Available online: https://constructionsoftware.trimble.com (accessed on 30 December 2022).
- Teletrac. Construction Telematics: A Complete Overview-Teletrac Navman US. Available online: https://www.teletracnavman.com (accessed on 30 December 2022).
- Duggal, V. Neural Engineering Devices–Decoding the Brain. 2022. Available online: https://www.engineersgarage.com (accessed on 30 December 2022).
- Wang, G.; Zhou, J. Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices. Comput. Intell. Neurosci. 2022, 2022, 3192552. [Google Scholar] [CrossRef] [PubMed]
- Baduge, S.K.; Thilakarathna, S.; Perera, J.S.; Arashpour, M.; Sharafi, P.; Teodosio, B.; Shringi, A.; Mendis, P. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Autom. Constr. 2022, 141, 104440. [Google Scholar] [CrossRef]
- Economy, C. The Circularity Gap Report 2023. 2023. Available online: https://www.circularity-gap.world (accessed on 30 December 2022).
- Antwi-Afari, P.; Ng, S.T.; Hossain, M. A review of the circularity gap in the construction industry through scientometric analysis. J. Clean. Prod. 2021, 298, 126870. [Google Scholar] [CrossRef]
- Charef, R.; Lu, W.; Hall, D. The transition to the circular economy of the construction industry: Insights into sustainable approaches to improve the understanding. J. Clean. Prod. 2022, 364, 132421. [Google Scholar] [CrossRef]
- EMF. Universal Circular Economy Policy Goals: Enabling the Transition to Scale. 2021. Available online: https://ellenmacarthurfoundation.org (accessed on 30 December 2022).
- Bilal, M.; Oyedele, L.O.; Akinade, O.O.; Ajayi, S.O.; Alaka, H.A.; Owolabi, H.A.; Qadir, J.; Pasha, M.; Bello, S.A. Big data architecture for construction waste analytics (CWA): A conceptual framework. J. Build. Eng. 2016, 6, 144–156. [Google Scholar] [CrossRef]
- Shojaei, A.; Ketabi, R.; Razkenari, M.; Hakim, H.; Wang, J. Enabling a circular economy in the built environment sector through blockchain technology. J. Clean. Prod. 2021, 294, 126352. [Google Scholar] [CrossRef]
- Guerra, B.C.; Leite, F. Circular economy in the construction industry: An overview of United States stakeholders’ awareness, major challenges, and enablers. Resour. Conserv. Recycl. 2021, 170, 105617. [Google Scholar] [CrossRef]
- Shirowzhan, S.; Sepasgozar, S.M.E.; Edwards, D.J.; Li, H.; Wang, C. BIM compatibility and its differentiation with interoperability challenges as an innovation factor. Autom. Constr. 2020, 112, 103086. [Google Scholar] [CrossRef]
Name of Journal | Number of Articles |
---|---|
Automation in Construction (AiC) | 41 |
Engineering, Construction, and Architectural Management (ECAM) | 32 |
Buildings | 25 |
Construction Innovation (CI) | 21 |
Journal of Cleaner Production (JCLP) | 19 |
Journal of Information Technology in Construction (ITCon) | 13 |
Sustainability (Switzerland) | 12 |
Journal of Construction Engineering and Management (JCEM) | 12 |
Journal of Building Engineering (JBE) | 11 |
Journal of Management in Engineering (JME) | 10 |
Research Instruments | Number of Articles |
---|---|
Survey/Questionnaire | 32 |
Interviews | 20 |
Simulation/Modelling | 48 |
Workshops | 21 |
Case Studies | 32 |
Mixed Methods | 67 |
Reviews | 69 |
BIM | GIS | IoT | ML | AR | Semantic Web | BCT | Reality Capture | DT | |
---|---|---|---|---|---|---|---|---|---|
Deng et al. [52] | * | * | * | ||||||
Wong et al. [53] | * | * | * | * | |||||
Rausch et al. [54] | * | * | * | ||||||
Sijtsema et al. [12] | * | * | * | * | |||||
Malagnino et al. [55] | * | * | |||||||
Gheisari et al. [56] | * | * | |||||||
Deng et al. [57] | * | * | |||||||
Dave et al. [58] | * | * | * | ||||||
Das et al. [59] | * | * | * | ||||||
Chen et al. [60] | * | * | |||||||
Chen et al. [61] | * | * | * | ||||||
Williams et al. [62] | * | * | |||||||
Wang et al. [63] | * | * | * | ||||||
Nawari et al. [64] | * | * | * | ||||||
Khan et al. [22] | * | * | |||||||
He et al. [65] | * | * | |||||||
Darko et al. [16] | * | * | * | * | * | ||||
Alizadehsalehi et al. [66] | * | * | |||||||
Niu et al. [67] | * | * | * | ||||||
Pauwels et al. [68] | * | * | * |
Literature Content | Publish Year | Statements | Prospects |
---|---|---|---|
BIM and IoT integration for facility management (FM) [69] | 2021 | BIM and IoT integration research are still at the early stage, as the works stay at the conceptual level. | BIM: the interoperability of data needs to be improved for FM; the industry foundation class (IFC) open standards need to be reviewed for the information demand of FM |
BIM and IoT devices integration [70] | 2019 | The real-time data from IoT are connected to BIM models and the research about integration of BIM and IoT in the initial stage. The methods that have already been used are focused on BIM application programming interface (API) and relational database, query language, semantic web technologies, and hybrid approach. | Future research directions are suggested as service-oriented architecture patterns (SOA), web services-based strategies, standards establishment, cloud computing etc. |
BIM, GIS, and Web integration [71] | 2021 | The integration research of BIM, GIS, and Web is in the tendency to grow especially after 2016. | Future research gaps are integration interoperability solutions, standardization, model processing, data exchange etc. |
BIM and AR [72] | 2020 | The methods adopted in data capture for building site construction are fiducial markers, GIS, GPS, laser scanning, and photogrammetry. The integration of BIM and AR would enhance the visualization of the site and improve the information process for construction management. | It is recommended that the AR impacts on the quality, execution speed, loss reduction, and production increase of BIM-based projects are investigated. The validation of the integration model of AR and BIM needs to be implemented. |
BIM and Image-based technologies [73] | 2017 | Image-based technologies in data capture, object recognition, and as-is BIM construction are reviewed. | The challenges could be decreased cost for data capture, improved efficiency for data management, pre-designed methods for object recognition, and full automation for as-is BIM construction. |
BIM and BCT [74] | 2019 | The applications of blockchain in AEC industry and its incorporation with BIM are investigated. The distributed ledger technology (DLT) also improved BIM workflow on network security and data management, tracing, and ownership. | The hyperledger fabric (HLF) applications for enhancing automated code compliances in BIM workflow is the future prospect. |
BIM and GIS: IFC geometry transformation [75] | 2019 | To realize efficient data exchange for the integration of BIM and GIS, this work enhances the open-source approach (E-OSA), by developing an automatic multipitch generation (E-AMG) algorithm. | The E-OSA enhanced by E-AMG still requires human intervention; it should be improved in the future. |
BIM and machine learning integration [76] | 2021 | To improve information exchange for AEC projects and leverage data interpretation, the work proposed a system for property valuation. An integration method of BIM and machine learning is used, implementing database interpretation, IFC information extraction, and automated valuation model (AVMs). | The authors suggested infusion technology of BIM and other digital technologies like IoT, DT, BCT, cloud computing, machine learning and so on could be used for property valuation and the AEC industry. |
Themes | Current Agenda, Emerging Concepts, and Technologies | Future Directions and Practices in 2030s |
---|---|---|
Sustainable construction and net-zero carbon emission | Paris Agreement on Climate Change; sustainable development goals (SDGs); awareness and policy development; case studies; circular economy | Use electric equipment for autonomous operation Embrace net-zero carbon emission Utilize AIOT-based supply chain systems to neutralize carbon Eliminate the waste by using 3D printing, modular off-site construction, and autonomous robots Lighter-weight, easy to install or use, higher strength per weight |
Online and cloud technologies | Resilient during pandemic | Utilize remote control systems Users collaborate with robots |
User interfaces and applications covering the entire life cycle from design to performance | Semi-autonomous excavators/bulldozers for some repetitive tasks, accurizing terrain data, and measuring productivity | Utilize standardization, repetitive design, and modularization to enhance robots’ efficiency. Use of autonomous haulage systems and equipment |
Platforms and controlling systems | Digital technologies are used for the design and Engineering, Construction, and Operation phases. | Integrate BIM/GIS and blockchain to share the models and information with all stakeholders in all the phases Use intelligent contracts to decrease disputes and enhance the efficiency of communication among stakeholders |
Systems and dashboards | BIM and GIS are integrated into some projects. Interoperability and integration of various tools such as BIM, DT, blockchain, and the Metaverse. See more about the concept of interoperability [149] | Implement Open BIM and Open GIS. Extend collaboration in a cloud version. Develop connected BIM: e.g., BIM-GIS integrated with visualized dashboards that are easy to use by authorized stakeholders. BIM-GIS is a part of City Digital Twin and is used for performance optimization and impact assessment. Connect to sensors: BIM-GIS is connected to sensing technologies for the entire site, including heavy equipment and job-site tools. |
Digital/physical integration systems and immersive technologies (VR, AR, and MR) | Measuring and connecting, mainly on-directional data exchange and simulation, basic asset digital twins | Predict and develop expert systems Bi-directional data exchange Connect to robots and enable remote operations Sensor fusion and data integrity Integration between multiple technologies, standardization, multi-user collaboration, real-time analyses, and the Metaverse |
New materials, including nanomaterials driven by carbon fibers | Lighter-weight, easy to install or use, higher strength per weight Eco-efficient and eco-effective materials drawing on concepts of Circular Economy developed by Ellen MacArthur foundation | |
Ontology and semantic web | The basic logic for data management as shared conceptualization and complementary to BIM and DT | Serving for interoperability, linking data, and logical inference |
United Nations SDG U.S. Innovation and Competition Act (2021) Build Back Better Bill Industrial Internet of Things (IIoT) Free-market innovation R&D Tax incentives Green financing | Industry 4.0 Smart construction Digital transformation Horizontal integration | Made in China 2025 Fourth Industrial Revolution Construction manufacturing Cyber-physical systems Vertical integration Net zero Circular economy |
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Sepasgozar, S.M.E.; Khan, A.A.; Smith, K.; Romero, J.G.; Shen, X.; Shirowzhan, S.; Li, H.; Tahmasebinia, F. BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction. Buildings 2023, 13, 441. https://doi.org/10.3390/buildings13020441
Sepasgozar SME, Khan AA, Smith K, Romero JG, Shen X, Shirowzhan S, Li H, Tahmasebinia F. BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction. Buildings. 2023; 13(2):441. https://doi.org/10.3390/buildings13020441
Chicago/Turabian StyleSepasgozar, Samad M. E., Ayaz Ahmad Khan, Kai Smith, Juan Garzon Romero, Xiaohan Shen, Sara Shirowzhan, Heng Li, and Faham Tahmasebinia. 2023. "BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction" Buildings 13, no. 2: 441. https://doi.org/10.3390/buildings13020441
APA StyleSepasgozar, S. M. E., Khan, A. A., Smith, K., Romero, J. G., Shen, X., Shirowzhan, S., Li, H., & Tahmasebinia, F. (2023). BIM and Digital Twin for Developing Convergence Technologies as Future of Digital Construction. Buildings, 13(2), 441. https://doi.org/10.3390/buildings13020441